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The Lancet Digital Health
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2589-7500
Published by Elsevier Homepage  [3184 journals]
  • Toward clinically useful models for individualised prognostication in

    • Abstract: Publication date: Available online 12 September 2019Source: The Lancet Digital HealthAuthor(s): Nikolaos Koutsouleris
  • Development and validation of multivariable prediction models of
           remission, recovery, and quality of life outcomes in people with first
           episode psychosis: a machine learning approach

    • Abstract: Publication date: Available online 12 September 2019Source: The Lancet Digital HealthAuthor(s): Samuel P Leighton, Rachel Upthegrove, Rajeev Krishnadas, Michael E Benros, Matthew R Broome, Georgios V Gkoutos, Peter F Liddle, Swaran P Singh, Linda Everard, Peter B Jones, David Fowler, Vimal Sharma, Nicholas Freemantle, Rune H B Christensen, Nikolai Albert, Merete Nordentoft, Matthias Schwannauer, Jonathan Cavanagh, Andrew I Gumley, Max BirchwoodSummaryBackgroundOutcomes for people with first-episode psychosis are highly heterogeneous. Few reliable validated methods are available to predict the outcome for individual patients in the first clinical contact. In this study, we aimed to build multivariable prediction models of 1-year remission and recovery outcomes using baseline clinical variables in people with first-episode psychosis.MethodsIn this machine learning approach, we applied supervised machine learning, using regularised regression and nested leave-one-site-out cross-validation, to baseline clinical data from the English Evaluating the Development and Impact of Early Intervention Services (EDEN) study (n=1027), to develop and internally validate prediction models at 1-year follow-up. We assessed four binary outcomes that were recorded at 1 year: symptom remission, social recovery, vocational recovery, and quality of life (QoL). We externally validated the prediction models by selecting from the top predictor variables identified in the internal validation models the variables shared with the external validation datasets comprised of two Scottish longitudinal cohort studies (n=162) and the OPUS trial, a randomised controlled trial of specialised assertive intervention versus standard treatment (n=578).FindingsThe performance of prediction models was robust for the four 1-year outcomes of symptom remission (area under the receiver operating characteristic curve [AUC] 0·703, 95% CI 0·664–0·742), social recovery (0·731, 0·697–0·765), vocational recovery (0·736, 0·702–0·771), and QoL (0·704, 0·667–0·742; p
  • Effectiveness of school-based eHealth interventions to prevent multiple
           lifestyle risk behaviours among adolescents: a systematic review and

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): Katrina E Champion, Belinda Parmenter, Cyanna McGowan, Bonnie Spring, Q Eileen Wafford, Lauren A Gardner, Louise Thornton, Nyanda McBride, Emma L Barrett, Maree Teesson, Nicola C Newton, Cath Chapman, Tim Slade, Matthew Sunderland, Judy Bauer, Steve Allsop, Leanne Hides, Lexine Stapinksi, Louise Birrell, Louise MewtonSummaryBackgroundLifestyle risk behaviours typically emerge during adolescence, track into adulthood, and commonly co-occur. Interventions targeting multiple risk behaviours in adolescents have the potential to efficiently improve health outcomes, yet further evidence is required to determine their effect. We reviewed the effectiveness of eHealth school-based interventions targeting multiple lifestyle risk behaviours.MethodsIn this systematic review and meta-analysis, we searched Ovid MEDLINE, Embase, PsycINFO, and the Cochrane Library databases between Jan 1, 2000, and March 14, 2019, with no language restrictions, for publications on school-based eHealth multiple health behaviour interventions in humans. We also screened the grey literature for unpublished data. Eligible studies were randomised controlled trials of eHealth (internet, computers, tablets, mobile technology, or tele-health) interventions targeting two or more of six behaviours of interest: alcohol use, smoking, diet, physical activity, sedentary behaviour, and sleep. Primary outcomes of interest were the prevention or reduction of unhealthy behaviours, or improvement in healthy behaviours of the six behaviours. Outcomes were summarised in a narrative synthesis and combined using random-effects meta-analysis. This systematic review is registered with PROSPERO, identifier CRD42017072163.FindingsOf 10 571 identified records, 22 publications assessing 16 interventions were included, comprising 18 873 students, of whom on average 56·2% were female, with a mean age of 13·41 years (SD 1·52). eHealth school-based multiple health behaviour change interventions significantly increased fruit and vegetable intake (standard mean difference 0·11, 95% CI 0·03 to 0·19; p=0·007) and both accelerometer-measured (0·33, 0·05 to 0·61; p=0·02) and self-reported (0·14, 0·05 to 0·23; p=0·003) physical activity, and reduced screen time (−0·09, −0·17 to −0·01; p=0·03) immediately after the intervention; however, these effects were not sustained at follow-up when data were available. No effect was seen for alcohol or smoking, fat or sugar-sweetened beverage or snack consumption. No studies examined sleep or used mobile health interventions. The risk of bias in masking of final outcome assessors and selective outcome reporting was high or unclear across studies and overall we deemd the quality of evidence to be low to very low.InterpretationeHealth school-based interventions addressing multiple lifestyle risk behaviours can be effective in improving physical activity, screen time, and fruit and vegetable intake. However, effects were small and only evident immediately after the intervention. Further high quality, adolescent-informed research is needed to develop eHealth interventions that can modify multiple behaviours and sustain long-term effects.FundingPaul Ramsay Foundation and Australian National Health and Medical Research Council.
  • Chat-based instant messaging support integrated with brief interventions
           for smoking cessation: a community-based, pragmatic, cluster-randomised
           controlled trial

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): Man Ping Wang, Tzu Tsun Luk, Yongda Wu, William H Li, Derek Y Cheung, Antonio C Kwong, Vienna Lai, Sophia S Chan, Tai Hing LamSummaryBackgroundMobile instant messaging apps offer a modern way to deliver personalised smoking cessation support through real-time, interactive messaging (chat). In this trial, we aimed to assess the effect of chat-based instant messaging support integrated with brief interventions on smoking cessation in a cohort of smokers proactively recruited from the community.MethodsIn this two-arm, pragmatic, cluster-randomised controlled trial, we recruited participants aged 18 years or older who smoked at least one cigarette per day from 68 community sites in Hong Kong, China. Community sites were computer randomised (1:1) to the intervention group, in which participants received chat-based instant messaging support for 3 months, offers of referral to external smoking cessation services, and brief advice, or to the control group, in which participants received brief advice alone. The chat-based intervention included personalised behavioural support and promoted use of smoking cessation services. Masking of participants and the research team was not possible, but outcome assessors were masked to group assignment. The primary outcome was smoking abstinence validated by exhaled carbon monoxide concentrations lower than 4 parts per million and salivary cotinine concentrations lower than 10 ng/mL at 6 months after treatment initiation (3 months after the end of treatment). The primary analysis was by intention to treat and accounted for potential clustering effect by use of generalised estimating equation models. This trial is registered with, number NCT03182790.FindingsBetween June 18 and Sept 30, 2017, 1185 participants were randomly assigned to either the intervention (n=591) or control (n=594) groups. At the 6-month follow-up (77% of participants retained), the proportion of validated abstinence was significantly higher in the intervention group than in the control group (48 [8%] of 591 in intervention vs 30 [5%] of 594 in control group, unadjusted odds ratio 1·68, 95% CI 1·03–2·74; p=0·040). Engagement in the chat-based support in the intervention group was low (17%), but strongly predicted abstinence with or without use of external smoking cessation services.InterpretationChat-based instant messaging support integrated with brief cessation interventions increased smoking abstinence and could complement existing smoking cessation services.FundingHong Kong Council on Smoking and Health.
  • Automated deep learning design for medical image classification by
           health-care professionals with no coding experience: a feasibility study

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): Livia Faes, Siegfried K Wagner, Dun Jack Fu, Xiaoxuan Liu, Edward Korot, Joseph R Ledsam, Trevor Back, Reena Chopra, Nikolas Pontikos, Christoph Kern, Gabriella Moraes, Martin K Schmid, Dawn Sim, Konstantinos Balaskas, Lucas M Bachmann, Alastair K Denniston, Pearse A KeaneSummaryBackgroundDeep learning has the potential to transform health care; however, substantial expertise is required to train such models. We sought to evaluate the utility of automated deep learning software to develop medical image diagnostic classifiers by health-care professionals with no coding—and no deep learning—expertise.MethodsWe used five publicly available open-source datasets: retinal fundus images (MESSIDOR); optical coherence tomography (OCT) images (Guangzhou Medical University and Shiley Eye Institute, version 3); images of skin lesions (Human Against Machine [HAM] 10000), and both paediatric and adult chest x-ray (CXR) images (Guangzhou Medical University and Shiley Eye Institute, version 3 and the National Institute of Health [NIH] dataset, respectively) to separately feed into a neural architecture search framework, hosted through Google Cloud AutoML, that automatically developed a deep learning architecture to classify common diseases. Sensitivity (recall), specificity, and positive predictive value (precision) were used to evaluate the diagnostic properties of the models. The discriminative performance was assessed using the area under the precision recall curve (AUPRC). In the case of the deep learning model developed on a subset of the HAM10000 dataset, we did external validation using the Edinburgh Dermofit Library dataset.FindingsDiagnostic properties and discriminative performance from internal validations were high in the binary classification tasks (sensitivity 73·3–97·0%; specificity 67–100%; AUPRC 0·87–1·00). In the multiple classification tasks, the diagnostic properties ranged from 38% to 100% for sensitivity and from 67% to 100% for specificity. The discriminative performance in terms of AUPRC ranged from 0·57 to 1·00 in the five automated deep learning models. In an external validation using the Edinburgh Dermofit Library dataset, the automated deep learning model showed an AUPRC of 0·47, with a sensitivity of 49% and a positive predictive value of 52%.InterpretationAll models, except the automated deep learning model trained on the multilabel classification task of the NIH CXR14 dataset, showed comparable discriminative performance and diagnostic properties to state-of-the-art performing deep learning algorithms. The performance in the external validation study was low. The quality of the open-access datasets (including insufficient information about patient flow and demographics) and the absence of measurement for precision, such as confidence intervals, constituted the major limitations of this study. The availability of automated deep learning platforms provide an opportunity for the medical community to enhance their understanding in model development and evaluation. Although the derivation of classification models without requiring a deep understanding of the mathematical, statistical, and programming principles is attractive, comparable performance to expertly designed models is limited to more elementary classification tasks. Furthermore, care should be placed in adhering to ethical principles when using these automated models to avoid discrimination and causing harm. Future studies should compare several application programming interfaces on thoroughly curated datasets.FundingNational Institute for Health Research and Moorfields Eye Charity.
  • Bridging the digital divide in health care

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): Anita Makri
  • Snakebite and snake identification: empowering neglected communities and
           health-care providers with AI

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): Rafael Ruiz de Castañeda, Andrew M Durso, Nicolas Ray, José Luis Fernández, David J Williams, Gabriel Alcoba, François Chappuis, Marcel Salathé, Isabelle Bolon
  • Reconceptualising the digital maturity of health systems

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): Kathrin Cresswell, Aziz Sheikh, Marta Krasuska, Catherine Heeney, Bryony Dean Franklin, Wendy Lane, Hajar Mozaffar, Kathy Mason, Sally Eason, Susan Hinder, Henry W W Potts, Robin Williams
  • Turning the crank for machine learning: ease, at what expense'

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): Tom J Pollard, Irene Chen, Jenna Wiens, Steven Horng, Danny Wong, Marzyeh Ghassemi, Heather Mattie, Emily Lindmeer, Trishan Panch
  • Improving parkinsonism diagnosis with machine learning

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): Shawna Abel, Shannon Kolind
  • Improving eHealth intervention development and quality of evaluations

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): Marion Henderson, Craig Donnachie
  • Africa: opportunities for growth

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): The Lancet Digital Health
  • Development and validation of the automated imaging differentiation in
           parkinsonism (AID-P): a multicentre machine learning study

    • Abstract: Publication date: September 2019Source: The Lancet Digital Health, Volume 1, Issue 5Author(s): Derek B Archer, Justin T Bricker, Winston T Chu, Roxana G Burciu, Johanna L McCracken, Song Lai, Stephen A Coombes, Ruogu Fang, Angelos Barmpoutis, Daniel M Corcos, Ajay S Kurani, Trina Mitchell, Mieniecia L Black, Ellen Herschel, Tanya Simuni, Todd B Parrish, Cynthia Comella, Tao Xie, Klaus Seppi, Nicolaas I BohnenSummaryBackgroundDevelopment of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diffusion-weighted MRI can distinguish between parkinsonian syndromes using an automated imaging approach.MethodsWe did an international study at 17 MRI centres in Austria, Germany, and the USA. We used diffusion-weighted MRI from 1002 patients and the Movement Disorders Society Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) to develop and validate disease-specific machine learning comparisons using 60 template regions and tracts of interest in Montreal Neurological Institute space between Parkinson's disease and atypical parkinsonism (multiple system atrophy and progressive supranuclear palsy) and between multiple system atrophy and progressive supranuclear palsy. For each comparison, models were developed on a training and validation cohort and evaluated in an independent test cohort by quantifying the area under the curve (AUC) of receiving operating characteristic curves. The primary outcomes were free water and free-water-corrected fractional anisotropy across 60 different template regions.FindingsIn the test cohort for disease-specific comparisons, the diffusion-weighted MRI plus MDS-UPDRS III model (Parkinson's disease vs atypical parkinsonism had an AUC 0·962; multiple system atrophy vs progressive supranuclear palsy AUC 0·897) and diffusion-weighted MRI only model had high AUCs (Parkinson's disease vs atypical parkinsonism AUC 0·955; multiple system atrophy vs progressive supranuclear palsy AUC 0·926), whereas the MDS-UPDRS III only models had significantly lower AUCs (Parkinson's disease vs atypical parkinsonism 0·775; multiple system atrophy vs progressive supranuclear palsy 0·582). These results indicate that a non-invasive imaging approach is capable of differentiating forms of parkinsonism comparable to current gold standard methods.InterpretationsThis study provides an objective, validated, and generalisable imaging approach to distinguish different forms of parkinsonian syndromes using multisite diffusion-weighted MRI cohorts. The diffusion-weighted MRI method does not involve radioactive tracers, is completely automated, and can be collected in less than 12 min across 3T scanners worldwide. The use of this test could positively affect the clinical care of patients with Parkinson's disease and parkinsonism and reduce the number of misdiagnosed cases in clinical trials.FundingNational Institutes of Health and Parkinson's Foundation.
  • Big data and health

    • Abstract: Publication date: Available online 29 August 2019Source: The Lancet Digital HealthAuthor(s): Michael Snyder, Wenyu Zhou
  • Enabling digital health companionship is better than empowerment

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): Jessica Morley, Luciano Floridi
  • Digital health technologies and health-care privatisation

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): Paul Webster
  • Correction to Lancet Digital Health 2019; 1: e106–07

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s):
  • Correction to Lancet Digital Health 2019; 1: e136–47

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s):
  • Practical guidance on artificial intelligence for health-care data

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): Marzyeh Ghassemi, Tristan Naumann, Peter Schulam, Andrew L Beam, Irene Y Chen, Rajesh Ranganath
  • Challenges in the design and regulatory approval of 3D-printed surgical
           implants: a two-case series

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): Koen Willemsen, Razmara Nizak, Herke Jan Noordmans, René M Castelein, Harrie Weinans, Moyo C KruytSummaryBackgroundAdditive manufacturing or three-dimensional (3D) printing of metal implants can provide novel solutions for difficult-to-treat conditions, yet legislation concerning patient-specific implants complicates the implementation of these techniques in daily practice. In this Article, we share our acquired knowledge of the logistical and legal challenges associated with the use of patient-specific 3D-printed implants to treat spinal instabilities.MethodsTwo patients with semiurgent cases of spinal instability presented to our hospital in the Netherlands. In case 1, severe kyphotic deformity of the thoracic spine due to neurofibromatosis type 1 had led to incomplete paralysis, and a strong metallic strut extending from C6 to T11 was deemed necessary to provide long-term anterior support. In case 2, the patient presented with progressive paralysis caused by cervicothoracic dissociation due to vanishing bone disease. As the C5–T1 vertebral bodies had mostly vanished, an implant spanning the anterior spine from C4 to T2 was required. Because of the complex and challenging nature of both cases, conventional approaches were deemed inadequate; instead, patient-specific implants were designed with use of CT scans and computer-aided design software, and 3D printed in titanium with direct metal printing. For each implant, to ensure patient safety, a comprehensive technical file (describing the clinical substantiation, technical and design considerations, risk analysis, manufacturing process, and labelling) was produced in collaboration with a university department certified for the development and manufacturing of medical devices. Because the implants were categorised as custom-made or personalised devices under the EU Medical Device Regulation, the usual procedures for review and approval of medical devices by a notified body were not required. Finite-element analyses, compression strength tests, and cadaveric experiments were also done to ensure the devices were safe to use.FindingsThe planning, design, production, and insertion of the 3D-printed personalised implant took around 6 months in the first patient, but, given the experience from the first case, only took around 6 weeks in the second patient. In both patients, the surgeries went as planned and good positioning of each implant was confirmed. Both patients were discharged home within 1 week after the surgery. In the first patient, a fatigue fracture occured in one of the conventional posterior fusion rods after 10 months, which we repaired, without any deformation of the spine or signs of failure of the personalised implant observed. No other adverse events occurred up to 25 months of follow-up in case 1 and 6 months of follow-up in case 2.InterpretationPatient-specific treatment approaches incorporating 3D-printed implants can be helpful in carefully selected cases when conventional methods are not an option. Comprehensive and efficient interactions between medical engineers and physicians are essential to establish well designed frameworks to navigate the logistical and regulatory aspects of technology development to ensure the safety and legal validity of patient-specific treatments. The framework described here could encourage physicians to treat (once untreatable) patients with novel personalised techniques.FundingInterreg VA Flanders—The Netherlands programme, Applied and Engineering Sciences research programme, the Netherlands Organisation for Scientific Research, and the Dutch Arthritis FoundationVideo
  • mHealth solutions for engaging smokers unmotivated to quit

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): Gina Rae Kruse
  • Deep learning in glaucoma: progress, but still lots to do

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): Felipe A Medeiros
  • Current state of 3D-printed custom-made spinal implants

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): Wen Jie Choy, Ralph J Mobbs
  • Data without borders

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): The Lancet Digital Health
  • Detection of glaucomatous optic neuropathy with spectral-domain optical
           coherence tomography: a retrospective training and validation
           deep-learning analysis

    • Abstract: Publication date: August 2019Source: The Lancet Digital Health, Volume 1, Issue 4Author(s): An Ran Ran, Carol Y Cheung, Xi Wang, Hao Chen, Lu-yang Luo, Poemen P Chan, Mandy O M Wong, Robert T Chang, Suria S Mannil, Alvin L Young, Hon-wah Yung, Chi Pui Pang, Pheng-Ann Heng, Clement C ThamSummaryBackgroundSpectral-domain optical coherence tomography (SDOCT) can be used to detect glaucomatous optic neuropathy, but human expertise in interpretation of SDOCT is limited. We aimed to develop and validate a three-dimensional (3D) deep-learning system using SDOCT volumes to detect glaucomatous optic neuropathy.MethodsWe retrospectively collected a dataset including 4877 SDOCT volumes of optic disc cube for training (60%), testing (20%), and primary validation (20%) from electronic medical and research records at the Chinese University of Hong Kong Eye Centre (Hong Kong, China) and the Hong Kong Eye Hospital (Hong Kong, China). Residual network was used to build the 3D deep-learning system. Three independent datasets (two from Hong Kong and one from Stanford, CA, USA), including 546, 267, and 1231 SDOCT volumes, respectively, were used for external validation of the deep-learning system. Volumes were labelled as having or not having glaucomatous optic neuropathy according to the criteria of retinal nerve fibre layer thinning on reliable SDOCT images with position-correlated visual field defect. Heatmaps were generated for qualitative assessments.Findings6921 SDOCT volumes from 1 384 200 two-dimensional cross-sectional scans were studied. The 3D deep-learning system had an area under the receiver operation characteristics curve (AUROC) of 0·969 (95% CI 0·960–0·976), sensitivity of 89% (95% CI 83–93), specificity of 96% (92–99), and accuracy of 91% (89–93) in the primary validation, outperforming a two-dimensional deep-learning system that was trained on en face fundus images (AUROC 0·921 [0·905–0·937]; p
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